title: Methods of artificial enlargement of the training set for statistical shape models.
abstract: Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.
authors.affiliation: VTT Technical Research Centre of Finland, FIN- 33101 Tampere, Finland. juha.koikkalainen@vtt.fi
authors.forename: Juha
authors.initials: J
authors.lastname: Koikkalainen
authors.forename: Tuomas
authors.initials: T
authors.lastname: Tölli
authors.forename: Kirsi
authors.initials: K
authors.lastname: Lauerma
authors.forename: Kari
authors.initials: K
authors.lastname: Antila
authors.forename: Elina
authors.initials: E
authors.lastname: Mattila
authors.forename: Mikko
authors.initials: M
authors.lastname: Lilja
authors.forename: Jyrki
authors.initials: J
authors.lastname: Lötjönen
mesh_terms: D019412:Anatomy, Cross-Sectional / Q000379:methods; D001185:Artificial Intelligence; D001330:Electronic Data Processing / Q000379:methods; D020342:Finite Element Analysis; D006321:Heart / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis; D012107:Research Design / Q000706:statistics & numerical data; D018401:Sample Size